def on_epoch_end(self, last_metrics, **kwargs): prec = self._precision() rec = self._recall() metric = (1 + self.beta2) * prec * rec / (prec * self.beta2 + rec + self.eps) metric[metric != metric] = 0 # removing potential "nan"s if self.avg: metric = (self._weights(avg=self.avg) * metric).sum() return add_metrics(last_metrics, metric)
def on_epoch_end(self, last_metrics, **kwargs): label = np.concatenate(self._label) score = np.concatenate(self._score) if len(set(label)) != 2: auc = 0.5 else: score = np.concatenate(self._score) auc = roc_auc_score(label,score) return add_metrics(last_metrics, auc)
def on_epoch_end(self, last_metrics, **kwargs): return add_metrics(last_metrics, self._precision())
def on_epoch_end(self, last_metrics, **kwargs): return add_metrics(last_metrics, self._recall())
def on_epoch_end(self, last_metrics, **kwargs): return add_metrics( last_metrics, self.compute_f1(self.tp, self.fp, self.fn))